Monitoring environmental conditions such as temperature, humidity, air pressure, light, motion, gas levels, VOC levels, and sound is important and useful in a number of applications and situations, especially in settings such as scientific laboratories, clinical and medical settings, manufacturing plants, and warehouses. In addition to monitoring ambient conditions in entire rooms, it is also useful to monitor such parameters in smaller spaces such as inside refrigerators, freezers, incubators, storage boxes, fume hoods, and the like. Many sensors that are used to measure environmental parameters are designed to measure a single environmental parameter, such as temperature or humidity. Some monitoring systems can be configured to set off an alarm when a sensor's reading crosses a given threshold or falls outside of the limits of some normal operating range. However, these systems provide no insight as to why the alarm condition was reached. For example, many existing temperature sensors are configured to raise an alarm or send an alert if the temperature crosses, or transgresses, a given threshold without taking into account factors such as the duration of the time outside the desired limits and the extent to which the temperature has changed beyond a threshold limit. Some temperature sensors are equipped with more intelligence so that minor transgressions, for example, in duration and extent of temperature change, do not trigger an alarm condition. However, since these alerting and alarm systems generally rely on a single type of environmental sensor, e.g., a temperature sensor, they are not able to determine the context in which a transgression may have occurred. It is preferable to be able to determine more information about why a transgression has occurred and more preferable to be able to predict that a transgression is likely to happen in the future, and further more preferable to be able to predict when a transgression is likely to happen. The ability to predict such events can be improved when data from multiple sensors, multiple types of sensors, and multiple sensors of multiple types are combined.
Because existing sensor systems provide no contextual information, if the alarm is a true alarm, it is often received too late to take appropriate action to address the failure. Further, no information is provided to distinguish false alarms from true alarms, or true alarms that are unnecessary or premature because conditions are corrected within an acceptable period of time. Thus, there exists a need for more intelligent sensor systems that can discriminate between fluctuations in environmental parameters due to normal usage and activity and those due to abnormal events, and reduces premature and false or unnecessary alarms. There is also a need for systems that can predict when an environmental parameter will likely go outside of desired limits and advise a user of a timeframe in which this is expected to occur, so that corrective action can be taken.